## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1027279 54.9 2127124 113.7 1296541 69.3
## Vcells 1721196 13.2 8388608 64.0 2221245 17.0
Definitions
|
Ciudad
|
Trip
|
Collection
|
Questionnaire
|
|
Bogota2019
|
Moving from one part to another with a specific reason/motive, a definite hour of start and end, a mode of transport, and a duration greater than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration
|
Trips made the day of reference, i.e., the day before the survey.
|
…
|
|
Mexico
|
Moving from one part to another with a specific reason/motive, using one or multiples modes of transport
|
Trips made during the week (Tuesday, Wednesday, Thursday) and in Saturdays (weekends)
|
…
|
|
Cali
|
Moving from one part to another with a specific reason/motive and a duration longer than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration
|
Trips made the day of reference, i.e., the day before the survey.
|
…
|
|
Medellin
|
Couldn’t find the definition
|
Trips made the day of reference, i.e., last 24 hours
|
…
|
|
Santiago
|
Any movement carried out on public roads with a purpose determined, between two places (origin and destination) at a certain time of day; It can be carried out in several modes of transport and consist of one or more stages
|
Trips made in working days (regular season), in weekends (regular season) and in working days(summer season)
|
…
|
Summary table
|
|
Bogota2015
|
Bogota2019
|
Bogota2019_longer15
|
Mexico
|
Mexico_weekdays
|
Mexico_weekends
|
Medellin
|
Cali
|
Santiago
|
|
Min.
|
0.0
|
0.0
|
0.0
|
0.0
|
1
|
1.0
|
1.0
|
0.0
|
0.0
|
|
1st Qu.
|
14.0
|
15.0
|
25.0
|
20.0
|
15
|
15.0
|
15.0
|
10.0
|
15.0
|
|
Median
|
32.2
|
30.0
|
45.0
|
43.0
|
30
|
30.0
|
30.0
|
25.0
|
30.0
|
|
Mean
|
39.7
|
50.6
|
58.6
|
52.4
|
43
|
43.2
|
33.7
|
42.2
|
36.9
|
|
3rd Qu.
|
61.6
|
60.0
|
75.0
|
75.0
|
60
|
60.0
|
45.0
|
45.0
|
50.0
|
|
Max.
|
553.7
|
1110.0
|
1110.0
|
1200.0
|
840
|
735.0
|
600.0
|
1282.0
|
1335.0
|
|
NA’s
|
22515.0
|
10319.0
|
13899.0
|
0.0
|
17964
|
37916.0
|
6494.0
|
12618.0
|
14066.0
|
Density plot
These plots are interactive so we can zoom in and out, and select cities.
ggplotly(
ggplot() +
geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 ,
data = bogota_2015) +
geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 ,
data = bogota_2019) +
geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 ,
data = bogota_2019_longer15) +
geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 ,
data = mexico) +
geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 ,
data = mexico_weekdays) +
geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 ,
data = mexico_weekends) +
geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 ,
data = medellin) +
geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 ,
data = cali) +
geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 ,
data = santiago)
)
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: Removed 10319 rows containing non-finite values (stat_density).
## Warning: Removed 13899 rows containing non-finite values (stat_density).
## Warning: Removed 17964 rows containing non-finite values (stat_density).
## Warning: Removed 37916 rows containing non-finite values (stat_density).
## Warning: Removed 6494 rows containing non-finite values (stat_density).
## Warning: Removed 12618 rows containing non-finite values (stat_density).
## Warning: Removed 14066 rows containing non-finite values (stat_density).
Density plot by mode
Bogota 2015
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = bogota_2015))
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
Bogota 2019
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = bogota_2019))
## Warning: Removed 10319 rows containing non-finite values (stat_density).
Bogota 2019 walking trips longer than 15 minutes
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = bogota_2019_longer15))
## Warning: Removed 13899 rows containing non-finite values (stat_density).
Mexico
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = mexico))
Mexico weekdays
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = mexico_weekdays))
## Warning: Removed 17964 rows containing non-finite values (stat_density).
Mexico weekends
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = mexico_weekends))
## Warning: Removed 37916 rows containing non-finite values (stat_density).
Medellin
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = medellin))
## Warning: Removed 6494 rows containing non-finite values (stat_density).
Cali
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = cali))
## Warning: Removed 12618 rows containing non-finite values (stat_density).
Santiago
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = santiago))
## Warning: Removed 14066 rows containing non-finite values (stat_density).
Comparison of walking trips
ggplotly(
ggplot() +
geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 ,
data = bogota_2015 %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 ,
data = bogota_2019 %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 ,
data = bogota_2019_longer15 %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 ,
data = mexico %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 ,
data = mexico_weekdays %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 ,
data = mexico_weekends %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 ,
data = medellin %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 ,
data = cali %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 ,
data = santiago %>%
filter(trip_mode == "walk"))
)
## Warning: Removed 4 rows containing non-finite values (stat_density).
## Warning: Removed 1 rows containing non-finite values (stat_density).
## Warning: Removed 398 rows containing non-finite values (stat_density).
## Warning: Removed 2 rows containing non-finite values (stat_density).
Comparison of cycling trips
ggplotly(
ggplot() +
geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 ,
data = bogota_2015 %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 ,
data = bogota_2019 %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 ,
data = bogota_2019_longer15 %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 ,
data = mexico %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 ,
data = mexico_weekdays %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 ,
data = mexico_weekends %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 ,
data = medellin %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 ,
data = cali %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 ,
data = santiago %>%
filter(trip_mode == "bicycle"))
)
## Warning: Removed 40 rows containing non-finite values (stat_density).